System with a data aggregation module generating aggregated data for responding to OLAP analysis queries in a user transparent manner

ABSTRACT

A system for supporting OLAP analysis over a network. The system comprises an OLAP server for enabling an OLAP user to perform OLAP analysis via interaction with a client machine on the network. The system also includes a data aggregation module comprising a multi-dimensional datastore, an aggregation engine integrated with the multi-dimensional datastore, and a first interface for loading base data from a data source to the aggregation engine. The aggregation engine performs data aggregation operations on loaded base data, generates aggregated data from the base data, and stores the aggregated data in the multi-dimensional datastore. A second interface receives requests for OLAP analysis from the OLAP server, accesses the aggregation engine to retrieve from the multi-dimensional datastore, aggregated data corresponding to requests, and communicates the retrieved aggregated data to the OLAP server for query servicing, in a manner transparent to the OLAP user.

RELATED CASES

This is a Continuation of U.S. application Ser. No. 10/314,884 filedDec. 9, 2002; which is a Continuation of U.S. application Ser. No.09/796,098 filed Feb. 28, 2001; which is a Continuation of U.S.application Ser. No. 10/314,884 filed Dec. 9, 2002; which is aContinuation of U.S. application Ser. No. 09/796,098 filed Feb. 28,2001; which is a Continuation-in-part of: copending U.S. applicationSer. No. 09/514,611 filed Feb. 28, 2000, now U.S. Pat. No. 6,434,544,and U.S. application Ser. No. 09/634,748 filed Aug. 9, 2000, now U.S.Pat. No. 6,385,604; each said Application being commonly owned byHyperRoll, Limited, and incorporated herein by reference in itsentirety.

BACKGROUND OF THE INVENTION

1. Field of Invention

The present invention relates to a method of and system for aggregatingdata elements in a multi-dimensional database (MDDB) supported upon acomputing platform and also to provide an improved method of and systemfor managing data elements within a MDDB during on-line analyticalprocessing (OLAP) operations and as an integral part of a databasemanagement system.

2. Brief Description of the State of the Art

The ability to act quickly and decisively in today's increasinglycompetitive marketplace is critical to the success of organizations. Thevolume of information that is available to corporations is rapidlyincreasing and frequently overwhelming. Those organizations that willeffectively and efficiently manage these tremendous volumes of data, anduse the information to make business decisions, will realize asignificant competitive advantage in the marketplace.

Data warehousing, the creation of an enterprise-wide data store, is thefirst step towards managing these volumes of data. The Data Warehouse isbecoming an integral part of many information delivery systems becauseit provides a single, central location where a reconciled version ofdata extracted from a wide variety of operational systems is stored.Over the last few years, improvements in price, performance,scalability, and robustness of open computing systems have made datawarehousing a central component of Information Technology CITstrategies. Details on methods of data integration and constructing datawarehouses can be found in the white paper entitled “Data Integration:The Warehouse Foundation” by Louis Roilleigh and Joe Thomas.

Building a Data Warehouse has its own special challenges (e.g. usingcommon data model, common business dictionary, etc.) and is a complexendeavor. However, just having a Data Warehouse does not provideorganizations with the often-heralded business benefits of datawarehousing. To complete the supply chain from transactional systems todecision maker, organizations need to deliver systems that allowknowledge workers to make strategic and tactical decisions based on theinformation stored in these warehouses. These decision support systemsare referred to as On-Line Analytical Processing (OLAP) systems. OLAPsystems allow knowledge workers to intuitively, quickly, and flexiblymanipulate operational data using familiar business terms, in order toprovide analytical insight into a particular problem or line of inquiry.For example, by using an OLAP system, decision makers can “slice anddice” information along a customer (or business) dimension, and viewbusiness metrics by product and through time. Reports can be definedfrom multiple perspectives that provide a high-level or detailed view ofthe performance of any aspect of the business. Decision makers cannavigate throughout their database by drilling down on a report to viewelements at finer levels of detail, or by pivoting to view reports fromdifferent perspectives. To enable such full-functioned businessanalyses, OLAP systems need to (1) support sophisticated analyses, (2)scale to large numbers of dimensions, and (3) support analyses againstlarge atomic data sets. These three key requirements are discussedfurther below.

Decision makers use key performance metrics to evaluate the operationswithin their domain, and OLAP systems need to be capable of deliveringthese metrics in a user-customizable format. These metrics may beobtained from the transactional databases pre-calculated and stored inthe database, or generated on demand during the query process. Commonlyused metrics include:

-   -   (1) Multidimensional Ratios (e.g. Percent to Total)—“Show me the        contribution to weekly sales and category profit made by all        items sold in the Northwest stores between July 1 and July 14.”    -   (2) Comparisons (e.g. Actual vs. Plan, This Period vs. Last        Period)—“Show me the sales to plan percentage variation for this        year and compare it to that of the previous year to identify        planning discrepancies.”    -   (3) Ranking and Statistical Profiles (e.g. Top N/Bottom N,        70/30, Quartiles)—“Show me sales, profit and average call volume        per day for my 20 most profitable salespeople, who are in the        top 30% of the worldwide sales.”    -   (4) Custom Consolidations—“Show me an abbreviated income        statement by quarter for the last two quarters for my Western        Region operations.”

Knowledge workers analyze data from a number of different businessperspectives or dimensions. As used hereinafter, a dimension is anyelement or hierarchical combination of elements in a data model that canbe displayed orthogonally with respect to other combinations of elementsin the data model. For example, if a report lists sales by week,promotion, store, and department, then the report would be a slice ofdata taken from a four-dimensional data model.

Target marketing and market segmentation applications involve extractinghighly qualified result sets from large volumes of data. For example, adirect marketing organization might want to generate a targeted mailinglist based on dozens of characteristics, including purchase frequency,size of the last purchase, past buying trends, customer location, age ofcustomer, and gender of customer. These applications rapidly increasethe dimensionality requirements for analysis.

The number of dimensions in OLAP systems range from a few orthogonaldimensions to hundreds of orthogonal dimensions. Orthogonal dimensionsin an exemplary OLAP application might include Geography, Time, andProducts.

Atomic data refers to the lowest level of data granularity required foreffective decision making. In the case of a retail merchandisingmanager, “atomic data” may refer to information by store, by day, and byitem. For a banker, atomic data may be information by account, bytransaction, and by branch. Most organizations implementing OLAP systemsfind themselves needing systems that can scale to tens, hundreds, andeven thousands of gigabytes of atomic information.

As OLAP systems become more pervasive and are used by the majority ofthe enterprise, more data over longer time frames will be included inthe data store (i.e. data warehouse), and the size of the database willincrease by at least an order of magnitude. Thus, OLAP systems need tobe able to scale from present to near-future volumes of data.

In general, OLAP systems need to (1) support the complex analysisrequirements of decision-makers, (2) analyze the data from a number ofdifferent perspectives (i.e. business dimensions), and (3) supportcomplex analyses against large input (atomic-level) data sets from aData Warehouse maintained by the organization using a relationaldatabase management system (RDBMS).

Vendors of OLAP systems classify OLAP Systems as either Relational OLAP(ROLAP) or Multidimensional OLAP (MOLAP) based on the underlyingarchitecture thereof. Thus, there are two basic architectures forOn-Line Analytical Processing systems: the ROLAP Architecture, and theMOLAP architecture.

The Relational OLAP (ROLAP) system accesses data stored in a DataWarehouse to provide OLAP analyses. The premise of ROLAP is that OLAPcapabilities are best provided directly against the relational database,i.e. the Data Warehouse.

The ROLAP architecture was invented to enable direct access of data fromData Warehouses, and therefore support optimization techniques to meetbatch window requirements and provide fast response times. Typically,these optimization techniques include application-level tablepartitioning, pre-aggregate inferencing, denormalization support, andthe joining of multiple fact tables.

A typical prior art ROLAP system has a three-tier or layer client/serverarchitecture. The “database layer” utilizes relational databases fordata storage, access, and retrieval processes. The “application logiclayer” is the ROLAP engine which executes the multidimensional reportsfrom multiple users. The ROLAP engine integrates with a variety of“presentation layers,” through which users perform OLAP analyses.

After the data model for the data warehouse is defined, data fromon-line transaction-processing (OLTP) systems is loaded into therelational database management system (RDBMS). If required by the datamodel, database routines are run to pre-aggregate the data within theRDBMS. Indices are then created to optimize query access times. Endusers submit multidimensional analyses to the ROLAP engine, which thendynamically transforms the requests into SQL execution plans. The SQLexecution plans are submitted to the relational database for processing,the relational query results are cross-tabulated, and a multidimensionalresult data set is returned to the end user. ROLAP is a fully dynamicarchitecture capable of utilizing pre-calculated results when they areavailable, or dynamically generating results from atomic informationwhen necessary.

Multidimensional OLAP (MOLAP) systems utilize a proprietarymultidimensional database (MDDB) to provide OLAP analyses. The MDDB islogically organized as a multidimensional array (typically referred toas a multidimensional cube or hypercube or cube) whose rows/columns eachrepresent a different dimension (i.e., relation). A data value isassociated with each combination of dimensions (typically referred to asa “coordinate”). The main premise of this architecture is that data mustbe stored multidimensionally to be accessed and viewedmultidimensionally.

As shown in FIG. 1B, prior art MOLAP systems have an Aggregation, Accessand Retrieval module which is responsible for all data storage, access,and retrieval processes, including data aggregration (i.e.preaggregation) in the MDDB. As shown in FIG. 1B, the base data loaderis fed with base data, in the most detailed level, from the DataWarehouse, into the Multi-Dimensional Data Base (MDDB). On top of thebase data, layers of aggregated data are built-up by the Aggregationprogram, which is part of the Aggregation, Access and Retrieval module.As indicated in this figure, the application logic module is responsiblefor the execution of all OLAP requests/queries (e.g. ratios, ranks,forecasts, exception scanning, and slicing and dicing) of data withinthe MDDB. The presentation module integrates with the application logicmodule and provides an interface, through which the end users view andrequest OLAP analyses on their client machines which may be web-enabledthrough the infrastructure of the Internet. The client/serverarchitecture of a MOLAP system allows multiple users to access the samemultidimensional database (MDDB).

Information (i.e. basic data) from a variety of operational systemswithin an enterprise, comprising the Data Warehouse, is loaded into aprior art multidimensional database (MDDB) through a series of batchroutines. The Express™ server by the Oracle Corporation is exemplary ofa popular server which can be used to carry out the data loading processin prior art MOLAP systems. As shown in FIG. 2B, an exemplary 3-D MDDBis schematically depicted, showing geography, time and products as the“dimensions” of the database. The multidimensional data of the MDDB islogically organized in an array structure, as shown in FIG. 2C.Physically, the Express™ server stores data in pages (or records) of aninformation file. Pages contain 512, or 2048, or 4096 bytes of data,depending on the platform and release of the Express™ server. In orderto look up the physical record address from the database file recordedon a disk or other mass storage device, the Express™ server generates adata structure referred to as a “Page Allocation Table (PAT)”. As shownin FIG. 2D, the PAT tells the Express™ server the physical record numberthat contains the page of data. Typically, the PAT is organized inpages. The simplest way to access a data element in the MDDB is bycalculating the “offset” using the additions and multiplicationsexpressed by a simple formula:Offset=Months+Product*(# of_Months)+City*(# of_Months*# of_Products)

During an OLAP session, the response time of a multidimensional query ona prior art MDDB depends on how many cells in the MDDB have to be added“on the fly”. As the number of dimensions in the MDDB increaseslinearly, the number of the cells in the MDDB increases exponentially.However, it is known that the majority of multidimensional queries dealwith summarized high level data. Thus, as shown in FIGS. 3A and 3B, oncethe atomic data (i.e. “basic data”) has been loaded into the MDDB, thegeneral approach is to perform a series of calculations in batch inorder to aggregate (i.e. pre-aggregate) the data elements along theorthogonal dimensions of the MDDB and fill the array structures thereof.For example, revenue figures for all retail stores in a particular state(i.e. New York) would be added together to fill the state level cells inthe MDDB. After the array structure in the database has been filled,integer-based indices are created and hashing algorithms are used toimprove query access times. Pre-aggregation of dimension D0 is alwaysperformed along the cross-section of the MDDB along the D0 dimension.

As shown in FIGS. 3C1 and 3C2, the raw data loaded into the MDDB isprimarily organized at its lowest dimensional hierarchy, and the resultsof the pre-aggregations are stored in the neighboring parts of the MDDB.

As shown in FIG. 3C 2, along the TIME dimension, weeks are theaggregation results of days, months are the aggregation results ofweeks, and quarters are the aggregation results of months. While notshown in the figures, along the GEOGRAPHY dimension, states are theaggregation results of cities, countries are the aggregation results ofstates, and continents are the aggregation results of countries. Bypre-aggregating (i.e. consolidating or compiling) all logical subtotalsand totals along all dimensions of the MDDB, it is possible to carry outreal-time MOLAP operations using a multidimensional database (MDDB)containing both basic (i.e. atomic) and pre-aggregated data. Once thiscompilation process has been completed, the MDDB is ready for use. Usersrequest OLAP reports by submitting queries through the OLAP Applicationinterface (e.g. using web-enabled client machines), and the applicationlogic layer responds to the submitted queries by retrieving the storeddata from the MDDB for display on the client machine.

Typically, in MDDB systems, the aggregated data is very sparse, tendingto explode as the number of dimension grows and dramatically slowingdown the retrieval process (as described in the report entitled“Database Explosion: The OLAP Report”, incorporated herein byreference). Quick and on line retrieval of queried data is critical indelivering on-line response for OLAP queries. Therefore, the datastructure of the MDDB, and methods of its storing, indexing and handlingare dictated mainly by the need of fast retrieval of massive and sparsedata.

Different solutions for this problem are disclosed in the following USpatents, each of which is incorporated herein by reference in itsentirety:

-   -   U.S. Pat. No. 5,822,751 “Efficient Multidimensional Data        Aggregation Operator Implementation”    -   U.S. Pat. No. 5,805,885 “Method And System For Aggregation        Objects”    -   U.S. Pat. No. 5,781,896 “Method And System For Efficiently        Performing Database Table Aggregation Using An Aggregation        Index”    -   U.S. Pat. No. 5,745,764 “Method And System For Aggregation        Objects”

In all the prior art OLAP servers, the process of storing, indexing andhandling MDDB utilize complex data structures to largely improve theretrieval speed, as part of the querying process, at the cost of slowingdown the storing and aggregation. The query-bounded structure, that mustsupport fast retrieval of queries in a restricting environment of highsparcity and multi-hierarchies, is not the optimal one for fastaggregation.

In addition to the aggregation process, the Aggregation, Access andRetrieval module is responsible for all data storage, retrieval andaccess processes. The Logic module is responsible for the execution ofOLAP queries. The Presentation module intermediates between the user andthe logic module and provides an interface through which the end usersview and request OLAP analyses. The client/server architecture allowsmultiple users to simultaneously access the multidimensional database.

In summary, general system requirements of OLAP systems include: (1)supporting sophisticated analysis, (2) scaling to large number ofdimensions, and (3) supporting analysis against large atomic data sets.

MOLAP system architecture is capable of providing analyticallysophisticated reports and analysis functionality. However, requirements(2) and (3) fundamentally limit MOLAP's capability, because to beeffective and to meet end-user requirements, MOLAP databases need a highdegree of aggregation.

By contrast, the ROLAP system architecture allows the construction ofsystems requiring a low degree of aggregation, but such systems aresignificantly slower than systems based on MOLAP system architectureprinciples. The resulting long aggregation times of ROLAP systems imposesevere limitations on its volumes and dimensional capabilities.

The graphs plotted in FIG. 5 clearly indicate the computational demandsthat are created when searching an MDDB during an OLAP session, whereanswers to queries are presented to the MOLAP system, and answersthereto are solicited often under real-time constraints. However, priorart MOLAP systems have limited capabilities to dynamically create dataaggregations or to calculate business metrics that have not beenprecalculated and stored in the MDDB.

The large volumes of data and the high dimensionality of certain marketsegmentation applications are orders of magnitude beyond the limits ofcurrent multidimensional databases.

ROLAP is capable of higher data volumes. However, the ROLAParchitecture, despite its high volume and dimensionality superiority,suffers from several significant drawbacks as compared to MOLAP:

-   -   Full aggregation of large data volumes are very time consuming,        otherwise, partial aggregation severely degrades the query        response.    -   It has a slower query response    -   It requires developers and end users to know SQL    -   SQL is less capable of the sophisticated analytical        functionality necessary for OLAP    -   ROLAP provides limited application functionality

Thus, improved techniques for data aggregation within MOLAP systemswould appear to allow the number of dimensions of and the size of atomic(i.e. basic) data sets in the MDDB to be significantly increased, andthus increase the usage of the MOLAP system architecture.

Also, improved techniques for data aggregation within ROLAP systemswould appear to allow for maximized query performance on large datavolumes, and reduce the time of partial aggregations that degrades queryresponse, and thus generally benefit ROLAP system architectures.

Thus, there is a great need in the art for an improved way of and meansfor aggregating data elements within a multi-dimensional database(MDDB), while avoiding the shortcomings and drawbacks of prior artsystems and methodologies.

Modern operational and informational database systems, as describedabove, typically use a database management system (DBMS) (such as anRDBMS system, object database system, or object/relational databasesystem) as a repository for storing data and querying the data. FIG. 14illustrates a data warehouse-OLAP domain that utilizes the prior artapproaches described above. The data warehouse is an enterprise-widedata store. It is becoming an integral part of many information deliverysystems because it provides a single, central location wherein areconciled version of data extracted from a wide variety of operationalsystems is stored. Details on methods of data integration andconstructing data warehouses can be found in the white paper entitled“Data Integration: The Warehouse Foundation” by Louis Rollleigh and JoeThomas.

Building a Data Warehouse has its own special challenges (e.g. usingcommon data model, common business dictionary, etc.) and is a complexendeavor. However, just having a Data Warehouse does not provideorganizations with the often-heralded business benefits of datawarehousing. To complete the supply chain from transactional systems todecision maker, organizations need to deliver systems that allowknowledge workers to make strategic and tactical decisions based on theinformation stored in these warehouses. These decision support systemsare referred to as On-Line Analytical Processing (OLAP) systems. SuchOLAP systems are commonly classified as Relational OLAP systems orMulti-Dimensional OLAP systems as described above.

The Relational OLAP (ROLAP) system accesses data stored in a relationaldatabase (which is part of the Data Warehouse) to provide OLAP analyses.The premise of ROLAP is that OLAP capabilities are best provideddirectly against the relational database. The ROLAP architecture wasinvented to enable direct access of data from Data Warehouses, andtherefore support optimization techniques to meet batch windowrequirements and provide fast response times. Typically, theseoptimization techniques include application-level table partitioning,pre-aggregate inferencing, denormalization support, and the joining ofmultiple fact tables.

As described above, a typical ROLAP system has a three-tier or layerclient/server architecture. The “database layer” utilizes relationaldatabases for data storage, access, and retrieval processes. The“application logic layer” is the ROLAP engine which executes themultidimensional reports from multiple users. The ROLAP engineintegrates with a variety of “presentation layers,” through which usersperform OLAP analyses. After the data model for the data warehouse isdefined, data from on-line transaction-processing (OLTP) systems isloaded into the relational database management system (RDBMS). Ifrequired by the data model, database routines are run to pre-aggregatethe data within the RDBMS. Indices are then created to optimize queryaccess times. End users submit multidimensional analyses to the ROLAPengine, which then dynamically transforms the requests into SQLexecution plans. The SQL execution plans are submitted to the relationaldatabase for processing, the relational query results arecross-tabulated, and a multidimensional result data set is returned tothe end user. ROLAP is a fully dynamic architecture capable of utilizingpre-calculated results when they are available, or dynamicallygenerating results from the raw information when necessary.

The Multidimensional OLAP (MOLAP) systems utilize a proprietarymultidimensional database (MDDB) (or “cube”) to provide OLAP analyses.The main premise of this architecture is that data must be storedmultidimensionally to be accessed and viewed multidimensionally. SuchMOLAP systems provide an interface that enables users to query the MDDBdata structure such that users can “slice and dice” the aggregated data.As shown in FIG. 15, such MOLAP systems have an aggregation engine whichis responsible for all data storage, access, and retrieval processes,including data aggregation (i.e. pre-aggregation) in the MDDB, and ananalytical processing and GUI module responsible for interfacing with auser to provide analytical analysis, query input, and reporting of queryresults to the user. In a relational database, data is stored in tables.In contrast, the MDDB is a non-relational data structure—it uses otherdata structures, either instead of or in addition to tables—to storedata.

There are other application domains where there is a great need forimproved methods of and apparatus for carrying out data aggregationoperations. For example, modern operational and informational databasesrepresent such domains. As described above, modern operational andinformational databases typically utilize a relational database system(RDBMS) as a repository for storing data and querying data. FIG. 16Aillustrates an exemplary table in an RDBMS; and FIGS. 16B and 16Cillustrate operators (queries) on the table of FIG. 16A, and the resultof such queries, respectively. The operators illustrated in FIGS. 16Band 16C are expressed as Structured Query Language (SQL) statements asis conventional in the art.

The choice of using a RDBMS as the data repository in informationdatabase systems naturally stems from the realities of SQLstandardization, the wealth of RDBMS-related tools, and readilyavailable expertise in RDBMS systems. However, the querying component ofRDBMS technology suffers from performance and optimization problemsstemming from the very nature of the relational data model. Morespecifically, during query processing, the relational data modelrequires a mechanism that locates the raw data elements that match thequery. Moreover, to support queries that involve aggregation operations,such aggregation operations must be performed over the raw data elementsthat match the query. For large multi-dimensional databases, a naiveimplementation of these operations involves computational intensivetable scans that leads to unacceptable query response times.

In order to better understand how the prior art has approached thisproblem, it will be helpful to briefly describe the relational databasemodel. According to the relational database model, a relational databaseis represented by a logical schema and tables that implement the schema.The logical schema is represented by a set of templates that define oneor more dimensions (entities) and attributes associated with a givendimension. The attributes associated with a given dimension includes oneor more attributes that distinguish it from every other dimension in thedatabase (a dimension identifier). Relationships amongst dimensions areformed by joining attributes. The data structure that represents the setof templates and relations of the logical schema is typically referredto as a catalog or dictionary. Note that the logical schema representsthe relational organization of the database, but does not hold any factdata per se. This fact data is stored in tables that implement thelogical schema.

Star schemas are frequently used to represent the logical structure of arelational database. The basic premise of star schemas is thatinformation can be classified into two groups: facts and dimensions.Facts are the core data elements being analyzed. For example, units ofindividual item sold are facts, while dimensions are attributes aboutthe facts. For example, dimensions are the product types purchased andthe data purchase. Business questions against this schema are askedlooking up specific facts (UNITS) through a set of dimensions (MARKETS,PRODUCTS, PERIOD). The central fact table is typically much larger thanany of its dimension tables.

An exemplary star schema is illustrated in FIG. 17A for suppliers (the“Supplier” dimension) and parts (the “Parts” dimension) over timeperiods (the “Time-Period” dimension). It includes a central fact table“Supplied-Parts” that relates to multiple dimensions—the “Supplier”,“Parts” and “Time-Period” dimensions. A central fact table and adimension table for each dimension in the logical schema of FIG. 17A maybe used to implement this logical schema. A given dimension table storesrows (instances) of the dimension defined in the logical schema. Eachrow within the central fact table includes a multi-part key associatedwith a set of facts (in this example, a number representing a quantity).The multi-part key of a given row (values stored in the S#, P#, TP#fields as shown) points to rows (instances) stored in the dimensiontables described above. A more detailed description of star schemas andthe tables used to implement star schemas may be found in C. J. Date,“An Introduction to Database Systems,” Seventh Edition, Addison-Wesley,2000, pp. 711-715, herein incorporated by reference in its entirety.

When processing a query, the tables that implement the schema areaccessed to retrieve the facts that match the query. For example, in astar schema implementation as described above, the facts are retrievedfrom the central fact table and/or the dimension tables. Locating thefacts that match a given query involves one or more join operations.Moreover, to support queries that involve aggregation operations, suchaggregation operations must be performed over the facts that match thequery. For large multi-dimensional databases, a naive implementation ofthese operations involves computational intensive table scans thattypically leads to unacceptable query response times. Moreover, sincethe fact tables are pre-summarized and aggregated along businessdimensions, these tables tend to be very large. This point becomes animportant consideration of the performance issues associated with starschemas. A more detailed discussion of the performance issues (andproposed approaches that address such issues) related to joining andaggregation of star schema is now set forth.

The first performance issue arises from computationally intensive tablescans that are performed by a naive implementation of data joining.Indexing schemes may be used to bypass these scans when performingjoining operations. Such schemes include B-tree indexing, inverted listindexing and aggregate indexing. A more detailed description of suchindexing schemes can be found in “The Art of Indexing”, DynamicInformation Systems Corporation, October 1999, available athttp://www.disc.com/artindex.pdf. All of these indexing schemes replacestable scan operations (involved in locating the data elements that matcha query) with one or more index lookup operation. Inverted list indexingassociates an index with a group of data elements, and stores (at alocation identified by the index) a group of pointers to the associateddata elements. During query processing, in the event that the querymatches the index, the pointers stored in the index are used to retrievethe corresponding data elements pointed therefrom. Aggregation indexingintegrates an aggregation index with an inverted list index to providepointers to raw data elements that require aggregation, therebyproviding for dynamic summarization of the raw data elements that matchthe user-submitted query.

These indexing schemes are intended to improve join operations byreplacing table scan operations with one or more index lookup operationin order to locate the data elements that match a query. However, theseindexing schemes suffer from various performance issues as follows:

-   -   Since the tables in the star schema design typically contain the        entire hierarchy of attributes (e.g. in a PERIOD dimension, this        hierarchy could be day>week>month>quarter>year), a multipart key        of day, week, month, quarter, year has to be created; thus,        multiple meta-data definitions are required (one of each key        component) to define a single relationship; this adds to the        design complexity, and sluggishness in performance.    -   Addition or deletion of levels in the hierarchy will require        physical modification of the fact table, which is time consuming        process that limits flexibility.    -   Carrying all the segments of the compound dimensional key in the        fact table increases the size of the index, thus impacting both        performance and scalability.

Another performance issue arises from dimension tables that containmultiple hierarchies. In such cases, the dimensional table oftenincludes a level of hierarchy indicator for every record. Everyretrieval from fact table that stores details and aggregates must usethe indicator to obtain the correct result, which impacts performance.The best alternative to using the level indicator is the snowflakeschema. In this schema aggregate tables are created separately from thedetail tables. In addition to the main fact tables, snowflake schemacontains separate fact tables for each level of aggregation. Notably,the snowflake schema is even more complicated than a star schema, andoften requires multiple SQL statements to get the results that arerequired.

Another performance issue arises from the pairwise join problem.Traditional RDBMS engines are not design for the rich set of complexqueries that are issued against a star schema. The need to retrieverelated information from several tables in a single query—“joinprocessing”—is severely limited. Many RDBMSs can join only two tables ata time. If a complex join involves more than two tables, the RDBMS needsto break the query into a series of pairwise joins. Selecting the orderof these joins has a dramatic performance impact. There are optimizersthat spend a lot of CPU cycles to find the best order in which toexecute those joins. Unfortunately, because the number of combinationsto be evaluated grows exponentially with the number of tables beingjoined, the problem of selecting the best order of pairwise joins rarelycan be solved in a reasonable amount of time.

Moreover, because the number of combinations is often too large,optimizers limit the selection on the basis of a criterion of directlyrelated tables. In a star schema, the fact table is the only tabledirectly related to most other tables, meaning that the fact table is anatural candidate for the first pairwise join. Unfortunately, the facttable is the very largest table in the query, so this strategy leads toselecting a pairwise join order that generates a very large intermediateresult set, severely affecting query performance.

There is an optimization strategy, typically referred to as CartesianJoins, that lessens the performance impact of the pairwise join problemby allowing joining of unrelated tables. The join to the fact table,which is the largest one, is deferred until the very end, thus reducingthe size of intermediate result sets. In a join of two unrelated tablesevery combination of the two tables' rows is produced, a Cartesianproduct. Such a Cartesian product improves query performance. However,this strategy is viable only if the Cartesian product of dimension rowsselected is much smaller than the number of rows in the fact table. Themultiplicative nature of the Cartesian join makes the optimizationhelpful only for relatively small databases.

In addition, systems that exploit hardware and software parallelism havebeen developed that lessens the performance issues set forth above.Parallelism can help reduce the execution time of a single query(speed-up), or handle additional work without degrading execution time(scale-up).). For example, Red Brick M has developed STARjoin™technology that provides high speed, parallelizable multi-table joins ina single pass, thus allowing more than two tables can be joined in asingle operation. The core technology is an innovative approach toindexing that accelerates multiple joins. Unfortunately, parallelism canonly reduce, not eliminate, the performance degradation issues relatedto the star schema.

One of the most fundamental principles of the multidimensional databaseis the idea of aggregation. The most common aggregation is called aroll-up aggregation. This type is relatively easy to compute: e.g.taking daily sales totals and rolling them up into a monthly salestable. The more difficult are analytical calculations, the aggregationof Boolean and comparative operators. However these are also consideredas a subset of aggregation.

In a star schema, the results of aggregation are summary tables.Typically, summary tables are generated by database administrators whoattempt to anticipate the data aggregations that the users will request,and then pre-build such tables. In such systems, when processing auser-generated query that involves aggregation operations, the pre-builtaggregated data that matches the query is retrieved from the summarytables (if such data exists). FIGS. 18A and 18B illustrate amulti-dimensional relational database using a star schema and summarytables. In this example, the summary tables are generated over the“time” dimension storing aggregated data for “month”, “quarter” and“year” time periods as shown in FIG. 18B. Summary tables are in essenceadditional fact tables, of higher levels. They are attached to the basicfact table creating a snowflake extension of the star schema. There arehierarchies among summary tables because users at different levels ofmanagement require different levels of summarization. Choosing the levelof aggregation is accomplished via the “drill-down” feature.

Summary tables containing pre-aggregated results typically provide forimproved query response time with respect to on-the-fly aggregation.However, summary tables suffer from some disadvantages:

-   -   summary tables require that database administrators anticipate        the data aggregation operations that users will require; this is        a difficult task in large multi-dimensional databases (for        example, in data warehouses and data mining systems), where        users always need to query in new ways looking for new        information and patterns.    -   summary tables do not provide a mechanism that allows efficient        drill down to view the raw data that makes up the summary        table—typically a table scan of one or more large tables is        required.    -   querying is delayed until pre-aggregation is completed.    -   there is a heavy time overhead because the vast majority of the        generated information remains unvisited.    -   there is a need to synchronize the summary tables before the        use.    -   the degree of viable parallelism is limited because the        subsequent levels of summary tables must be performed in        pipeline, due to their hierarchies.    -   for very large databases, this option is not valid because of        time and storage space.        Note that it is common to utilize both pre-aggregated results        and on-the-fly aggregation in support aggregation. In these        system, partial pre-aggregation of the facts results in a small        set of summary tables. On-the-fly aggregation is used in the        case the required aggregated data does not exist in the summary        tables.

Note that in the event that the aggregated data does not exist in thesummary tables, table join operations and aggregation operations areperformed over the raw facts in order to generate such aggregated data.This is typically referred to as on-the-fly aggregation. In suchinstances, aggregation indexing is used to mitigate the performance ofmultiple data joins associated with dynamic aggregation of the raw data.Thus, in large multi-dimensional databases, such dynamic aggregation maylead to unacceptable query response times.

In view of the problems associated with joining and aggregation withinRDBMS, prior art ROLAP systems have suffered from essentially the sameshortcomings and drawbacks of their underlying RDBMS.

While prior art MOLAP systems provide for improved access time toaggregated data within their underlying MDD structures, and haveperformance advantages when carrying out joining and aggregationsoperations, prior art MOLAP architectures have suffered from a number ofshortcomings and drawbacks. More specifically, atomic (raw) data ismoved, in a single transfer, to the MOLAP system for aggregation,analysis and querying. Importantly, the aggregation results are externalto the DBMS. Thus, users of the DBMS cannot directly view these results.Such results are accessible only from the MOLAP system. Because the MDDquery processing logic in prior art MOLAP systems is separate from thatof the DBMS, users must procure rights to access to the MOLAP system andbe instructed (and be careful to conform to such instructions) to accessthe MDD (or the DBMS) under certain conditions. Such requirements canpresent security issues, highly undesirable for system administration.Satisfying such requirements is a costly and logistically cumbersomeprocess. As a result, the widespread applicability of MOLAP systems hasbeen limited.

Thus, there is a great need in the art for an improved mechanism forjoining and aggregating data elements within a database managementsystem (e.g., RDBMS), and for integrating the improved databasemanagement system (e.g., RDBMS) into informational database systems(including the data warehouse and OLAP domains), while avoiding theshortcomings and drawbacks of prior art systems and methodologies.

SUMMARY AND OBJECTS OF PRESENT INVENTION

Accordingly, it is a further object of the present invention to providean improved method of and system for managing data elements within amultidimensional database (MDDB) using a novel stand-alone (i.e.external) data aggregation server, achieving a significant increase insystem performance (e.g. deceased access/search time) using astand-alone scalable data aggregation server.

Another object of the present invention is to provide such system,wherein the stand-alone aggregation server includes an aggregationengine that is integrated with an MDDB, to provide a cartridge-styleplug-in accelerator which can communicate with virtually anyconventional OLAP server.

Another object of the present invention is to provide such a stand-alonedata aggregration server whose computational tasks are restricted todata aggregation, leaving all other OLAP functions to the MOLAP serverand therefore complementing OLAP server's functionality.

Another object of the present invention is to provide such a system,wherein the stand-alone aggregation server carries out an improvedmethod of data aggregation within the MDDB which enables the dimensionsof the MDDB to be scaled up to large numbers and large atomic (i.e.base) data sets to be handled within the MDDB.

Another object of the present invention is to provide such a stand-aloneaggregration server, wherein the aggregation engine supportshigh-performance aggregation (i.e. data roll-up) processes to maximizequery performance of large data volumes, and to reduce the time ofpartial aggregations that degrades the query response.

Another object of the present invention is to provide such astand-alone, external scalable aggregation server, wherein itsintegrated data aggregation (i.e. roll-up) engine speeds up theaggregation process by orders of magnitude, enabling larger databaseanalysis by lowering the aggregation times.

Another object of the present invention is to provide such a novelstand-alone scalable aggregation server for use in OLAP operations,wherein the scalability of the aggregation server enables (i) the speedof the aggregation process carried out therewithin to be substantiallyincreased by distributing the computationally intensive tasks associatedwith data aggregation among multiple processors, and (ii) the large datasets contained within the MDDB of the aggregation server to besubdivided among multiple processors thus allowing the size of atomic(i.e. basic) data sets within the MDDB to be substantially increased.

Another object of the present invention is to provide such a novelstand-alone scalable aggregation server, which provides for uniform loadbalancing among processors for high efficiency and best performance, andlinear scalability for extending the limits by adding processors.

Another object of the present invention is to provide a stand-alone,external scalable aggregation server, which is suitable for MOLAP aswell as for ROLAP system architectures.

Another object of the present invention is to provide a novelstand-alone scalable aggregation server, wherein an MDDB and aggregationengine are integrated and the aggregation engine carries out ahigh-performance aggregation algorithm and novel storing and searchingmethods within the MDDB.

Another object of the present invention is to provide a novelstand-alone scalable aggregation server which can be supported onsingle-processor (i.e. sequential or serial) computing platforms, aswell as on multi-processor (i.e. parallel) computing platforms.

Another object of the present invention is to provide a novelstand-alone scalable aggregation server which can be used as acomplementary aggregation plug-in to existing MOLAP and ROLAP databases.

Another object of the present invention is to provide a novelstand-alone scalable aggregation server which carries out an novelrollup (i.e. down-up) and spread down (i.e. top-down) aggregationalgorithms.

Another object of the present invention is to provide a novelstand-alone scalable aggregation server which includes an integratedMDDB and aggregation engine which carries out full pre-aggregationand/or “on-the-fly” aggregation processes within the MDDB.

Another object of the present invention is to provide such a novelstand-alone scalable aggregation server which is capable of supportingMDDB having a multi-hierarchy dimensionality.

Another object of the present invention is to provide a novel method ofaggregating multidimensional data of atomic data sets originating from aRDBMS Data Warehouse.

Another object of the present invention is to provide a novel method ofaggregating multidimensional data of atomic data sets originating fromother sources, such as external ASCII files, MOLAP server, or other enduser applications.

Another object of the present invention is to provide a novelstand-alone scalable data aggregation server which can communicate withany MOLAP server via standard ODBC, OLE DB or DLL interface, in acompletely transparent manner with respect to the (client) user, withoutany time delays in queries, equivalent to storage in MOLAP server'scache.

Another object of the present invention is to provide a novel“cartridge-style” (stand-alone) scalable data aggregation engine whichdramatically expands the boundaries of MOLAP into large-scaleapplications including Banking, Insurance, Retail and PromotionAnalysis.

Another object of the present invention is to provide a novel“cartridge-style” (stand-alone) scalable data aggregation engine whichdramatically expands the boundaries of high-volatility type ROLAPapplications such as, for example, the precalculation of data tomaximize query performance.

Another object of the present invention is to provide a generic plug-incartridge-type data aggregation component, suitable for all MOLAPsystems of different vendors, dramatically reducing their aggregationburdens.

Another object of the present invention is to provide a novel highperformance cartridge-type data aggregration server which, havingstandardized interfaces, can be plugged-into the OLAP system ofvirtually any user or vendor.

Another object of the present invention is to provide a novel“cartridge-style” (stand-alone) scalable data aggregation engine whichhas the capacity to convert long batch-type data aggregations intointeractive sessions.

In another aspect, it is an object of the present invention to providean improved method of and system for joining and aggregating dataelements integrated within a database management system (DBMS) using anon-relational multi-dimensional data structure (MDDB), achieving asignificant increase in system performance (e.g. deceased access/searchtime), user flexibility and ease of use.

Another object of the present invention is to provide such an DBMSwherein its integrated data aggregation module supports high-performanceaggregation (i.e. data roll-up) processes to maximize query performanceof large data volumes.

Another object of the present invention is to provide such an DBMSsystem, wherein its integrated data aggregation (i.e. roll-up) modulespeeds up the aggregation process by orders of magnitude, enablinglarger database analysis by lowering the aggregation times.

Another object of the present invention is to provide such a novel DBMSsystem for use in OLAP operations.

Another object of the present invention is to provide a novel DBMSsystem having an integrated aggregation module that carries out an novelrollup (i.e. down-up) and spread down (i.e. top-down) aggregationalgorithms.

Another object of the present invention is to provide a novel DBMSsystem having an integrated aggregation module that carries out fullpre-aggregation and/or “on-the-fly” aggregation processes.

Another object of the present invention is to provide a novel DBMSsystem having an integrated aggregation module which is capable ofsupporting a MDDB having a multi-hierarchy dimensionality.

These and other object of the present invention will become apparenthereinafter and in the Claims to Invention set forth herein.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more fully appreciate the objects of the present invention,the following Detailed Description of the Illustrative Embodimentsshould be read in conjunction with the accompanying Drawings, wherein:

FIG. 1A is a schematic representation of an exemplary prior artrelational on-line analytical processing (ROLAP) system comprising athree-tier or layer client/server architecture, wherein the first tierhas a database layer utilizing an RDBMS for data storage, access, andretrieval processes, the second tier has an application logic layer(i.e. the ROLAP engine) for executing the multidimensional reports frommultiple users, and the third tier integrates the ROLAP engine with avariety of presentation layers, through which users perform OLAPanalyses;

FIG. 1B is a schematic representation of a generalized embodiment of aprior art multidimensional on-line analytical processing (MOLAP) systemcomprising a base data loader for receiving atomic (i.e. base) data froma Data Warehouse realized by a RDBMS, an OLAP multidimensional database(MDDB), an aggregation, access and retrival module, application logicmodule and presentation module associated with a conventional OLAP sever(e.g. Oracle's Express Server) for supporting on-line transactionalprocessing (OLTP) operations on the MDDB, to service database queriesand requests from a plurality of OLAP client machines typicallyaccessing the system from an information network (e.g. the Internet);

FIG. 2A is a schematic representation of the Data Warehouse shown in theprior art system of FIG. 1B comprising numerous data tables (e.g. T1, T2. . . Tn) and data field links, and the OLAP multidimensional databaseshown of FIG. 1B, comprising a conventional page allocation table (PAT)with pointers pointing to the physical storage of variables in aninformation storage device;

FIG. 2B is a schematic representation of an exemplary three-dimensionalMDDB and organized as a 3-dimensional Cartesian cube and used in theprior art system of FIG. 2A, wherein the first dimension of the MDDB isrepresentative of geography (e.g. cities, states, countries,continents), the second dimension of the MDDB is representative of time(e.g. days, weeks, months, years), the third dimension of the MDDB isrepresentative of products (e.g. all products, by manufacturer), and thebasic data element is a set of variables which are addressed by3-dimensional coordinate values;

FIG. 2C is a schematic representation of a prior art array structureassociated with an exemplary three-dimensional MDDB, arranged accordingto a dimensional hierarchy;

FIG. 2D is a schematic representation of a prior art page allocationtable for an exemplary three-dimensional MDDB, arranged according topages of data element addresses;

FIG. 3A is a schematic representation of a prior art MOLAP system,illustrating the process of periodically storing raw data in the RDBMSData Warehouse thereof, serially loading of basic data from the DataWarehouse to the MDDB, and the process of serially pre-aggregating (orpre-compiling) the data in the MDDB along the entire dimensionalhierarchy thereof;

FIG. 3B is a schematic representation illustrating that the Cartesianaddresses listed in a prior art page allocation table (PAT) point towhere physical storage of data elements (i.e. variables) occurs in theinformation recording media (e.g. storage volumes) associated with theMDDB, during the loading of basic data into the MDDB as well as duringdata preaggregation processes carried out therewithin;

FIG. 3C 1 is a schematic representation of an exemplarythree-dimensional database used in a conventional MOLAP system of theprior art, showing that each data element contained therein isphysically stored at a location in the recording media of the systemwhich is specified by the dimensions (and subdimensions within thedimensional hierarchy) of the data variables which are assignedinteger-based coordinates in the MDDB, and also that data elementsassociated with the basic data loaded into the MDDB are assigned lowerinteger coordinates in MDDB Space than pre-aggregated data elementscontained therewithin;

FIG. 3C 2 is a schematic representation illustrating that a conventionalhierarchy of the dimension of “time” typically contains thesubdimensions “days, weeks, months, quarters, etc.” of the prior art;

FIG. 3C 3 is a schematic representation showing how data elements havinghigher subdimensions of time in the MDDB of the prior art are typicallyassigned increased integer addresses along the time dimension thereof;

FIG. 4 is a schematic representation illustrating that, for very largeprior art MDDBs, very large page allocation tables (PATs) are requiredto represent the address locations of the data elements containedtherein, and thus there is a need to employ address data pagingtechniques between the DRAM (e.g. program memory) and mass storagedevices (e.g. recording discs or RAIDs) available on the serialcomputing platform used to implement such prior art MOLAP systems;

FIG. 5 is a graphical representation showing how search time in aconventional (i.e. prior art) MDDB increases in proportion to the amountof preaggregation of data therewithin;

FIG. 6A is a schematic representation of a generalized embodiment of amultidimensional on-line analytical processing (MOLAP) system of thepresent invention comprising a Data Warehouse realized as a relationaldatabase, a stand-alone Aggregration Server of the present inventionhaving an integrated aggregation engine and MDDB, and an OLAP serversupporting a plurality of OLAP clients, wherein the stand-aloneAggregation Server performs aggregation functions (e.g. summation ofnumbers, as well as other mathematical operations, such asmultiplication, subtraction, division etc.) and multi-dimensional datastorage functions;

FIG. 6B is a schematic block diagram of the stand-alone AggregationServer of the illustrative embodiment shown in FIG. 6A, showing itsprimary components, namely, a base data interface (e.g. OLDB, OLE-DB,ODBC, SQL, JDBC, API, etc.) for receiving RDBMS flat files lists andother files from the Data Warehouse (RDBMS), a base data loader forreceiving base data from the base data interface, configuration managerfor managing the operation of the base data interface and base dataloader, an aggregation engine and MDDB handler for receiving base datafrom the base loader, performing aggregation operations on the basedata, and storing the base data and aggregated data in the MDDB; anaggregation client interface (e.g. OLDB, OLE-DB, ODBC, SQL, JDBC, API,etc.) and input analyzer for receiving requests from OLAP clientmachines, cooperating with the aggregation engine and MDDB handler togenerate aggregated data and/or retrieve aggregated data from the MDDBthat pertains to the received requests, and returning this aggregatedback to the requesting OLAP clients; and a configuration manager formanaging the operation of the input analyzer and the aggregation clientinterface.

FIG. 6C is a schematic representation of the software modules comprisingthe aggregation engine and MDDB handler of the stand-alone AggregationServer of the illustrative embodiment of the present invention, showinga base data list structure being supplied to a hierarchy analysis andreorder module, the output thereof being transferred to an aggregationmanagement module, the output thereof being transferred to a storagemodule via a storage management module, and a Query Directed Roll-up(QDR) aggregation management module being provided for receivingdatabase (DB) requests from OLAP client machines (via the aggregationclient interface) and managing the operation of the aggregation andstorage management modules of the present invention;

FIG. 6D is a flow chart representation of the primary operations carriedout by the (DB) request serving mechanism within the QDR aggregationmanagement module shown in FIG. 6C;

FIG. 7A is a schematic representation of a separate-platform typeimplementation of the stand-alone Aggregation Server of the illustrativeembodiment of FIG. 6B and a conventional OLAP server supporting aplurality of client machines, wherein base data from a Data Warehouse isshown being received by the aggregation server, realized on a firsthardware/software platform (i.e. Platform A) and the stand-aloneAggregation Server is shown serving the conventional OLAP server,realized on a second hardware/software platform (i.e. Platform B), aswell as serving data aggregation requirements of other clientssupporting diverse applications such as spreadsheet, GUI front end, andapplications;

FIG. 7B is a schematic representation of a shared-platform typeimplementation of the stand-alone Aggregation Server of the illustrativeembodiment of FIG. 6B and a conventional OLAP server supporting aplurality of client machines, wherein base data from a Data Warehouse isshown being received by the stand-alone Aggregation Server, realized ona common hardware/software platform and the aggregation server is shownserving the conventional OLAP server, realized on the same commonhardware/software platform, as well as serving data aggregationrequirements of other clients supporting diverse applications such asspreadsheet, GUI front end, and applications;

FIG. 8A is a data table setting forth information representative ofperformance benchmarks obtained by the shared-platform typeimplementation of the stand-alone Aggregation Server of the illustrativeembodiment serving the conventional OLAP server (i.e. Oracle EXPRESSServer) shown in FIG. 7B, wherein the common hardware/software platformis realized using a Pentium II 450 Mhz, 1 GB RAM, 18 GB Disk, runningthe Microsoft NT operating system (OS);

FIG. 9A is a schematic representation of the first stage in the methodof segmented aggregation according to the principles of the presentinvention, showing initial aggregration along the 1st dimension;

FIG. 9B is a schematic representation of the next stage in the method ofsegmented aggregation according to the principles of the presentinvention, showing that any segment along dimension 1, such as the shownslice, can be separately aggregated along the remaining dimensions, 2and 3, and that in general, for an N dimensional system, the secondstage involves aggregation in N−1 dimensions. The principle ofsegementation can be applied on the first stage as well, however, only alarge enough data will justify such a sliced procedure in the firstdimension. Actually, it is possible to consider each segment as an N−1cube, enabling recursive computation.

FIG. 9C 1 is a schematic representation of the Query Directed Roll-up(QDR) aggregation method/procedure of the present invention, showingdata aggregation starting from existing basic data or previouslyaggregated data in the first dimension (D1), and such aggregated databeing utilized as a basis for QDR aggregation along the second dimension(D2);

FIG. 9C 2 is a schematic representation of the Query Directed Roll-up(QDR) aggregation method/procedure of the present invention, showinginitial data aggregation starting from existing previously aggregateddata in the second third (D3), and continuing along the third dimension(D3), and thereafter continuing aggregation along the second dimension(D2);

FIG. 10A is a schematic representation of the “slice-storage” method ofstoring sparse data in the disk storage devices of the MDDB of FIG. 6Bin accordance with the principles of the present invention, based on anascending-ordered index along aggregation direction, enabling fastretrieval of data;

FIG. 10B is a schematic representation of the data organization of datafiles and the directory file used in the storages of the MDDB of FIG.6B, and the method of searching for a queried data point therein using asimple binary search technique due to the data files ascending order;

FIG. 11A is a schematic representation of three exemplarymulti-hierarchical data structures for storage of data within the MDDBof FIG. 6B, having three levels of hierarchy, wherein the first levelrepresentative of base data is composed of items A, B, F, and G, thesecond level is composed of items C, E, H and I, and the third level iscomposed of a single item D, which is common to all three hierarchicalstructures;

FIG. 11B is a schematic representation of an optimizedmulti-hierarchical data structure merged from all three hierarchies ofFIG. 11A, in accordance with the principles of the present invention;

FIG. 11C(i) through 11C(ix) represent a flow chart description (andaccompanying data structures) of the operations of an exemplaryhierarchy transformation mechanism of the present invention thatoptimally merges multiple hierarchies into a single hierarchy that isfunctionally equivalent to the multiple hierarchies.

FIG. 12 is a schematic representation showing the levels of operationsperformed by the stand-alone Aggregation Server of FIG. 6B, summarizingthe different enabling components for carrying out the method ofsegmented aggregation in accordance with the principles of the presentinvention;

FIG. 13 is a schematic representation of the stand-alone AggregationServer of the present invention shown as a component of a central datawarehouse, serving the data aggregation needs of URL directory systems,Data Marts, RDBMSs, ROLAP systems and OLAP systems alike;

FIG. 14 is a schematic representation of a prior art informationdatabase system, wherein the present invention may be embodied;

FIG. 15 is a schematic representation of the prior art data warehouseand OLAP system, wherein the present invention may be embodied;

FIGS. 16A-16C are schematic representations of exemplary tables employedin a prior art Relational Database Management System (RDBMS); FIGS. 16Band 16C illustrate operators (queries) on the table of FIG. 16A, and theresult of such queries, respectively;

FIG. 17A is a schematic representation of an exemplary dimensionalschema (star schema) of a relational database;

FIG. 18A is a schematic representation of an exemplary multidimensionalschema (star schema);

FIG. 18B is a schematic representation of tables used to implement theschema of FIG. 18A, including summary tables storing results ofaggregation operations performed on the facts of the central fact tablealong the time-period dimension, in accordance with conventionalteachings;

FIG. 19A is a schematic representation of an exemplary embodiment of aDBMS (for example, an RDBMS as shown) of the present inventioncomprising a relational datastore and an integrated multidimensional(MDD) aggregation module supporting queries from a plurality of clients,wherein the aggregation engine performs aggregation functions (e.g.summation of numbers, as well as other mathematical operations, such asmultiplication, subtraction, division etc.) and non-relationalmulti-dimensional data storage functions.

FIG. 19B is a schematic block diagram of the MDD aggregation module ofthe illustrative embodiment of the present invention shown in FIG. 6A.

FIGS. 19C(i) and 19C(ii), taken together, set forth a flow chartrepresentation of the primary operations carried out within the DBMS ofthe present invention when performing data aggregation and relatedsupport operations, including the servicing of user-submitted (e.g.natural language) queries made on such aggregated database of thepresent invention.

FIG. 19D is a flow chart representation of the primary operationscarried out by the (DB) request serving mechanism within the MDD controlmodule shown in FIG. 6B.

FIG. 19E is a schematic representation of the view mechanism of an DBMSthat enables users to query on the aggregated data generated and/orstored in the MDD Aggregation module according to the present invention.

FIG. 19F is a schematic representation of the trigger mechanism of theDBMS that enables users to query on the aggregated data generated and/orstored in the MDD Aggregation module according to the present invention.

FIG. 19G is a schematic representation of the DBMS of the presentinvention, illustrating a logically partitioning into a relational partand a non-relational part. The relational part includes the relationaldata store (e.g., table(s) and dictionary) and support mechanisms (e.g.,query handling services). The non-relational part includes the MDDAggregation Module. Data flows bidirectionally between the relationalpart and the non-relational part as shown.

FIG. 20A shows a separate-platform type implementation of the DBMSsystem of the illustrative embodiment shown in FIG. 19A, wherein therelational datastore and support mechanisms (e.g., query handling, facttable(s) and dictionary of the DBMS) reside on a separate hardwareplatform and/or OS system from that used to run the MDD AggregationModule of the present invention.

FIG. 20B shows a common-platform type implementation of the DBMS systemof the illustrative embodiment shown in FIG. 19A, wherein the relationaldatastore and support mechanisms (e.g., query handling, fact table(s)and dictionary of the DBMS) share the same hardware platform andoperating system (OS) that is used to run the MDD Aggregation Module ofthe present invention.

FIG. 21 is a schematic representation of the DBMS of the presentinvention shown as a component of a central data warehouse, serving thedata storage and aggregation needs of a ROLAP system (or other OLAPsystem).

FIG. 22 is a schematic representation of the DBMS of the presentinvention shown as a component of a central data warehouse, wherein theDBMS includes integrated OLAP Analysis Logic (and preferably anintegrated Presentation Module) that operates cooperatively with thequery handling of the DBMS system and the MDD Aggregation Module toenable users of the DBMS system to execute multidimensional reports(e.g., ratios, ranks, transforms, dynamic consolidation, complexfiltering, forecasts, query governing, scheduling, flow control,pre-aggregate inferencing, denormalization support, and/or tablepartitioning and joins) and preferably perform traditional OLAP analyses(grids, graphs, maps, alerts, drill-down, data pivot, data surf, sliceand dice, print).

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS OF THE PRESENTINVENTION

Referring now to FIGS. 6A through 13, the preferred embodiments of themethod and system of the present invention will be now described ingreat detail hereinbelow, wherein like elements in the Drawings shall beindicated by like reference numerals.

Through this invention disclosure, the term “aggregation” and“preaggregation” shall be understood to mean the process of summation ofnumbers, as well as other mathematical operations, such asmultiplication, subtraction, division etc.

In general, the stand-alone aggregation server and methods of andapparatus for data aggregation of the present invention can be employedin a wide range of applications, including MOLAP systems, ROLAP systems,Internet URL-directory systems, personalized on-line e-commerce shoppingsystems, Internet-based systems requiring real-time control of packetrouting and/or switching, and the like.

For purposes of illustration, initial focus will be accorded toimprovements in MOLAP systems, in which knowledge workers are enabled tointuitively, quickly, and flexibly manipulate operational data within aMDDB using familiar business terms in order to provide analyticalinsight into a business domain of interest.

FIG. 6A illustrates a generalized embodiment of a multidimensionalon-line analytical processing (MOLAP) system of the present inventioncomprising: a Data Warehouse realized as a relational database; astand-alone cartridge-style Aggregation Server of the present inventionhaving an integrated aggregation engine and a MDDB; and an OLAP servercommunicating with the Aggregation Server, and supporting a plurality ofOLAP clients. In accordance with the principles of the presentinvention, the stand-alone Aggregation Server performs aggregationfunctions (e.g. summation of numbers, as well as other mathematicaloperations, such as multiplication, subtraction, division etc.) andmulti-dimensional data storage functions.

Departing from conventional practices, the principles of the presentinvention teaches moving the aggregation engine and the MDDB into aseparate Aggregation Server having standardized interfaces so that itcan be plugged-into the OLAP server of virtually any user or vendor.This dramatic move discontinues the restricting dependency ofaggregation from the analytical functions of OLAP, and by applying noveland independent algorithms. The stand-alone data aggregation serverenables efficient organization and handling of data, fast aggregationprocessing, and fast access to and retrieval of any data element in theMDDB.

As will be described in greater detail hereinafter, the AggregationServer of the present invention can serve the data aggregationrequirements of other types of systems besides OLAP systems such as, forexample, URL directory management Data Marts, RDBMS, or ROLAP systems.

The Aggregation Server of the present invention excels in performing twodistinct functions, namely: the aggregation of data in the MDDB; and thehandling of the resulting data base in the MDDB, for “on demand” clientuse. In the case of serving an OLAP server, the Aggregation Server ofthe present invention focuses on performing these two functions in ahigh performance manner (i.e. aggregating and storing base data,originated at the Data Warehouse, in a multidimensional storage (MDDB),and providing the results of this data aggregation process “on demand”to the clients, such as the OLAP server, spreadsheet applications, theend user applications. As such, the Aggregation Server of the presentinvention frees each conventional OLAP server, with which it interfaces,from the need of making data aggregations, and therefore allows theconventional OLAP server to concentrate on the primary functions of OLAPservers, namely: data analysis and supporting a graphical interface withthe user client.

FIG. 6B shows the primary components of the stand-alone AggregationServer of the illustrative embodiment, namely: a base data interface(e.g. OLDB, OLE-DB, ODBC, SQL, JDBC, API, etc.) for receiving RDBMS flatfiles lists and other files from the Data Warehouse (RDBMS), a base dataloader for receiving base data from the base data interface,configuration manager for managing the operation of the base datainterface and base data loader, an aggregation engine for receiving basedata from the base loader, a multi-dimensional database (MDDB); a MDDBhandler, an input analyzer, an aggregation client interface (e.g. OLDB,OLE-DB, ODBC, SQL, API, JDBC, etc.) and a configuration manager formanaging the operation of the input analyzer and the aggregation clientinterface.

During operation, the base data originates at data warehouse or othersources, such as external ASCII files, MOLAP server, or others. TheConfiguration Manager, in order to enable proper communication with allpossible sources and data structures, configures two blocks, the BaseData Interface and Data Loader. Their configuration is matched withdifferent standards such as OLDB, OLE-DB, ODBC, SQL, API, JDBC, etc.

As shown in FIG. 6B, the core of the data Aggregation Server of thepresent invention comprises: a data Aggregation Engine; aMultidimensional Data Handler (MDDB Handler); and a MultidimensionalData Storage (MDDB). The results of data aggregation are efficientlystored in the MDDB by the MDDB Handler.

As shown in FIGS. 6A and 6B, the stand-alone Aggregation Server of thepresent invention serves the OLAP Server (or other requesting computingsystem) via an aggregation client interface, which preferably conformsto standard interface protocols such as OLDB, OLE-DB, ODBC, SQL, JDBC,an API, etc. Aggregation results required by the OLAP server aresupplied on demand. Typically, the OLAP Server disintegrates the query,via parsing process, into series of requests. Each such request,specifying a n-dimensional coordinate, is presented to the AggregationServer. The Configuration Manager sets the Aggregation Client Interfaceand Input Analyzer for a proper communication protocol according to theclient user. The Input Analyzer converts the input format to make itsuitable for the MDDB Handler.

An object of the present invention is to make the transfer of datacompletely transparent to the OLAP user, in a manner which is equivalentto the storing of data in the MOLAP server's cache and without any querydelays. This requires that the stand-alone Aggregation Server haveexceptionally fast response characteristics. This object is enabled byproviding the unique data structure and aggregation mechanism of thepresent invention.

FIG. 6C shows the software modules comprising the aggregation engine andMDDB handler components of the stand-alone Aggregation Server of theillustrative embodiment. The base data list, as it arrives from RDBMS ortext files, has to be analyzed and reordered to optimize hierarchyhandling, according to the unique method of the present invention, asdescribed later with reference to FIGS. 11A and 11B.

The function of the aggregation management module is to administrate theaggregation process according to the method illustrated in FIGS. 9A and9B.

In accordance with the principles of the present invention, dataaggregation within the stand-alone Aggregation Server can be carried outeither as a complete pre-aggregation process, where the base data isfully aggregated before commencing querying, or as a query directedroll-up (QDR) process, where querying is allowed at any stage ofaggregation using the “on-the-fly” data aggregation process of thepresent invention. The QDR process will be described hereinafter ingreater detail with reference to FIG. 9C. The response to a request(i.e. a basic component of a client query), by calling the Aggregationmanagement module for “on-the-fly” data aggregation, or for accessingpre-aggregated result data via the Storage management module. Thequery/request serving mechanism of the present invention within the QDRaggregation management module is illustrated in the flow chart of FIG.6D.

The function of the Storage management module is to handlemultidimensional data in the storage(s) module in a very efficient way,according to the novel method of the present invention, which will bedescribed in detail hereinafter with reference to FIGS. 10A and 10B.

The request serving mechanism shown in FIG. 6D is controlled by the QDRaggregation management module. Requests are queued and served one byone. If the required data is already pre-calculated, then it isretrieved by the storage management module and returned to the client.Otherwise, the required data is calculated “on-the-fly” by theaggregation management module, and the result moved out to the client,while simultaneously stored by the storage management module, shown inFIG. 6C.

FIGS. 7A and 7B outline two different implementations of the stand-alone(cartridge-style) Aggregation Server of the present invention. In bothimplementations, the Aggregation Server supplies aggregated results to aclient.

FIG. 7A shows a separate-platform type implementation of the MOLAPsystem of the illustrative embodiment shown in FIG. 6A, wherein theAggregation Server of the present invention resides on a separatehardware platform and OS system from that used to run the OLAP server.In this type of implementation, it is even possible to run theAggregation Server and the OLAP Server on different-type operatingsystems (e.g. NT, Unix, MAC OS).

FIG. 7B shows a common-platform type implementation of the MOLAP systemof the illustrative embodiment shown in FIG. 6B, wherein the AggregationServer of the present invention and OLAP Server share the same hardwareplatform and operating system (OS).

FIG. 8A shows a table setting forth the benchmark results of anaggregation engine, implemented on a shared/common hardware platform andOS, in accordance with the principles of the present invention. Thecommon platform and OS is realized using a Pentium II 450 Mhz, 1 GB RAM,18 GB Disk, running the Microsoft NT operating system. The six (6) datasets shown in the table differ in number of dimensions, number ofhierarchies, measure of sparcity and data size. A comparison with ORACLEExpress, a major OLAP server, is made. It is evident that theaggregation engine of the present invention outperforms currentlyleading aggregation technology by more than an order of magnitude.

The segmented data aggregation method of the present invention isdescribed in FIGS. 9A through 9C2. These figures outline a simplifiedsetting of three dimensions only; however, the following analysisapplies to any number of dimensions as well.

The data is being divided into autonomic segments to minimize the amountof simultaneously handled data. The initial aggregation is practiced ona single dimension only, while later on the aggregation process involvesall other dimensions.

At the first stage of the aggregation method, an aggregation isperformed along dimension 1. The first stage can be performed on morethan one dimension. As shown in FIG. 9A, the space of the base data isexpanded by the aggregation process.

In the next stage shown in FIG. 9B, any segment along dimension 1, suchas the shown slice, can be separately aggregated along the remainingdimensions, 2 and 3. In general, for an N dimensional system, the secondstage involves aggregation in N−1 dimensions.

The principle of data segmentation can be applied on the first stage aswell. However, only a large enough data set will justify such a slicedprocedure in the first dimension. Actually, it is possible to considereach segment as an N−1 cube, enabling recursive computation.

It is imperative to get aggregation results of a specific slice beforethe entire aggregation is completed, or alternatively, to have theroll-up done in a particular sequence. This novel feature of theaggregation method of the present invention is that it allows thequerying to begin, even before the regular aggregation process isaccomplished, and still having fast response. Moreover, in relationalOLAP and other systems requiring only partial aggregations, the QDRprocess dramatically speeds up the query response.

The QDR process is made feasible by the slice-oriented roll-up method ofthe present invention. After aggregating the first dimension(s), themultidimensional space is composed of independent multidimensional cubes(slices). These cubes can be processed in any arbitrary sequence.

Consequently the aggregation process of the present invention can bemonitored by means of files, shared memory sockets, or queues tostatically or dynamically set the roll-up order.

In order to satisfy a single query coming from a client, before therequired aggregation result has been prepared, the QDR process of thepresent invention involves performing a fast on-the-fly aggregation(roll-up) involving only a thin slice of the multidimensional data.

FIG. 9C 1 shows a slice required for building-up a roll-up result of the2^(nd) dimension. In case 1, as shown, the aggregation starts from anexisting data, either basic or previously aggregated in the firstdimension. This data is utilized as a basis for QDR aggregation alongthe second dimension. In case 2, due to lack of previous data, a QDRinvolves an initial slice aggregation along dimension 3, and thereafteraggregation along the 2^(nd) dimension.

FIG. 9C 2 shows two corresponding QDR cases for gaining results in the3d dimension. Cases 1 and 2 differ in the amount of initial aggregationrequired in 2^(nd) dimension.

FIG. 10A illustrates the “Slice-Storage” method of storing sparse dataon storage disks. In general, this data storage method is based on theprinciple that an ascending-ordered index along aggregation direction,enables fast retrieval of data. FIG. 10A illustrates a unit-wide sliceof the multidimensional cube of data. Since the data is sparse, only fewnon-NA data points exist. These points are indexed as follows. The DataFile consists of data records, in which each n−1 dimensional slice isbeing stored, in a separate record. These records have a varying length,according to the amount of non-NA stored points. For each registeredpoint in the record, IND_(k) stands for an index in a n-dimensionalcube, and Data stands for the value of a given point in the cube.

FIG. 10B illustrates a novel method for randomly searching for a querieddata point in the MDDB of FIG. 6B by using a novel technique oforganizing data files and the directory file used in the storages of theMDDB, so that a simple binary search technique can then be employedwithin the Aggregation Server of the present invention. According tothis method, a metafile termed DIR File, keeps pointers to Data Files aswell as additional parameters such as the start and end addresses ofdata record (IND₀, IND_(n)), its location within the Data File, recordsize (n), file's physical address on disk (D_Path), and auxiliaryinformation on the record (Flags).

A search for a queried data point is then performed by an access to theDIR file. The search along the file can be made using a simple binarysearch due to file's ascending order. When the record is found, it isthen loaded into main memory to search for the required point,characterized by its index IND_(k). The attached Data field representsthe queried value. In case the exact index is not found, it means thatthe point is a NA.

In another aspect of the present invention, a novel method is providedfor optimally merging multiple hierarchies in multi-hierarchicalstructures. The method, illustrated in FIGS. 11A, 11B, and 11C ispreferably used by the Aggregation Server of the present invention inprocessing the table data (base data), as it arrives from RDBMS.

According to the devised method, the inner order of hierarchies within adimension is optimized, to achieve efficient data handling forsummations and other mathematical formulas (termed in general“Aggregation”). The order of hierarchy is defined externally. It isbrought from a data source to the stand-alone aggregation engine, as adescriptor of data, before the data itself. In the illustrativeembodiment, the method assumes hierarchical relations of the data, asshown in FIG. 11A. The way data items are ordered in the memory space ofthe Aggregation Server, with regard to the hierarchy, has a significantimpact on its data handling efficiency.

Notably, when using prior art techniques, multiple handling of dataelements, which occurs when a data element is accessed more than onceduring aggregation process, has been hitherto unavoidable when the mainconcern is to effectively handle the sparse data. The data structuresused in prior art data handling methods have been designed for fastaccess to a non NA data. According to prior art techniques, each accessis associated with a timely search and retrieval in the data structure.For the massive amount of data typically accessed from a Data Warehousein an OLAP application, such multiple handling of data elements hassignificantly degraded the efficiency of prior art data aggregationprocesses. When using prior art data handling techniques, the dataelement D shown in FIG. 11A must be accessed three times, causing pooraggregation performance.

In accordance with the data handling method of the present invention,the data is being pre-ordered for a singular handling, as opposed tomultiple handling taught by prior art methods. According to the presentinvention, elements of base data and their aggregated results arecontiguously stored in a way that each element will be accessed onlyonce. This particular order allows a forward-only handling, neverbackward. Once a base data element is stored, or aggregated result isgenerated and stored, it is never to be retrieved again for furtheraggregation. As a result the storage access is minimized. This way ofsingular handling greatly elevates the aggregation efficiency of largedata bases. An efficient handling method as used in the presentinvention, is shown in FIG. 7A. The data element D, as any otherelement, is accessed and handled only once.

FIG. 11A shows an example of a multi-hierarchical database structurehaving 3 hierarchies. As shown, the base data has a dimension thatincludes items A, B, F, and G. The second level is composed of items C,E, H and I. The third level has a single item D, which is common to allthree hierarchical structures. In accordance with the method of thepresent invention, a minimal computing path is always taken. Forexample, according to the method of the present invention, item D willbe calculated as part of structure 1, requiring two mathematicaloperations only, rather than as in structure 3, which would need fourmathematical operations. FIG. 11B depicts an optimized structure mergedfrom all three hierarchies.

FIG. 11C(i) through 11C(ix) represent a flow chart description (andaccompanying data structures) of the operations of an exemplaryhierarchy transformation mechanism of the present invention thatoptimally merges multiple hierarchies into a single hierarchy that isfunctionally equivalent to the multiple hierarchies. For the sake ofdescription, the data structures correspond to exemplary hierarchicalstructures described above with respect to FIGS. 11(A) and 11(B). Asillustrated in FIG. 11C(i), in step 1101, a catalogue is loaded from theDBMS system. As is conventional, the catalogue includes data (“hierarchydescriptor data”) describing multiple hierarchies for at least onedimension of the data stored in the DBMS. In step 1103, this hierarchydescriptor data is extracted from the catalogue. A loop (steps1105-1119) is performed over the items in the multiple hierarchydescribed by the hierarchy descriptor data.

In the loop 1105-1119, a given item in the multiple hierarchy isselected (step 1107); and, in step 1109, the parent(s) (ifany)—including grandparents, great-grandparents, etc.—of the given itemare identified and added to an entry (for the given item) in a parentlist data structure, which is illustrated in FIG. 11C(v). Each entry inthe parent list corresponds to a specific item and includes zero or moreidentifiers for items that are parents (or grandparents, orgreat-grandparents) of the specific item. In addition, an inner loop(steps 1111-1117) is performed over the hierarchies of the multiplehierarchies described by the hierarchy descriptor data, wherein in step1113 one of the multiple hierarchies is selected. In step 1115, thechild of the given item in the selected hierarchy (if any) is identifiedand added (if need be) to a group of identifiers in an entry (for thegiven item) in a child list data structure, which is illustrated in FIG.11C(vi). Each entry in the child list corresponds to a specific item andincludes zero or more groups of identifiers each identifying a child ofthe specific item. Each group corresponds to one or more of thehierarchies described by the hierarchy descriptor data.

The operation then continues to steps 1121 and 1123 as illustrated inFIG. 11C(ii) to verify the integrity of the multiple hierarchiesdescribed by the hierarchy descriptor data (step 1121) and fix (orreport to the user) any errors discovered therein (step 1123).Preferably, the integrity of the multiple hierarchies is verified instep 1121 by iteratively expanding each group of identifiers in thechild list to include the children, grandchildren, etc of any itemlisted in the group. If the child(ren) for each group for a specificitem do not match, a verification error is encountered, and such erroris fixed (or reported to the user (step 1123). The operation thenproceeds to a loop (steps 1125-1133) over the items in the child list.

In the loop (steps 1125-1133), a given item in the child list isidentified in step 1127. In step 1129, the entry in the child list forthe given item is examined to determine if the given item has nochildren (e.g., the corresponding entry is null). If so, the operationcontinues to step 1131 to add an entry for the item in level 0 of anordered list data structure, which is illustrated in FIG. 11C(vii);otherwise the operation continues to process the next item of the childlist in the loop. Each entry in a given level of the order listcorresponds to a specific item and includes zero or more identifierseach identifying a child of the specific item. The levels of the orderlist described the transformed hierarchy as will readily become apparentin light of the following. Essentially, loop 1125-1333 builds the lowestlevel (level 0) of the transformed hierarchy.

After loop 1125-1133, operation continues to process the lowest level toderive the next higher level, and iterate over this process to build outthe entire transformed hierarchy. More specifically, in step 1135, a“current level” variable is set to identify the lowest level. In step1137, the items of the “current level” of the ordered list are copied toa work list. In step 1139, it is determined if the worklist is empty. Ifso, the operation ends; otherwise operation continues to step 1141wherein a loop (steps 1141-1159) is performed over the items in the worklist.

In step 1143, a given item in the work list is identified and operationcontinues to an inner loop (steps 1145-1155) over the parent(s) of thegiven item (which are specified in the parent list entry for the givenitem). In step 1147 of the inner loop, a given parent of the given itemis identified. In step 1149, it is determined whether any other parent(e.g., a parent other than the given patent) of the given item is achild of the given parent (as specified in the child list entry for thegiven parent). If so, operation continues to step 1155 to process thenext parent of the given item in the inner loop; otherwise, operationcontinues to steps 1151 and 1153. In step 1151, an entry for the givenparent is added to the next level (current level+1) of the ordered list,if it does not exist there already. In step 1153, if no children of thegiven item (as specified in the entry for the given item in the currentlevel of the ordered list) matches (e.g., is covered by) any child (orgrandchild or great grandchild etc) of item(s) in the entry for thegiven parent in the next level of the ordered list, the given item isadded to the entry for the given parent in the next level of the orderedlist. Levels 1 and 2 of the ordered list for the example described aboveare shown in FIGS. 11C(viii) and 11C(ix), respectively. The children(including grandchildren and great grandchildren. etc) of an item in theentry for a given parent in the next level of the ordered list may beidentified by the information encoded in the lower levels of the orderedlist. After step 1153, operation continues to step 1155 to process thenext parent of the given item in the inner loop (steps 1145-1155)

After processing the inner loop (steps 1145-1155), operation continuesto step 1157 to delete the given item from the work list, and processingcontinues to step 1159 to process the next item of the work list in theloop (steps 1141-1159).

After processing the loop (steps 1141-1159), the ordered list (e.g.,transformed hierarchy) has been built for the next higher level. Theoperation continues to step 1161 to increment the current level to thenext higher level, and operation returns (in step 1163) to step 1138 tobuild the next higher level, until the highest level is reached(determined in step 1139) and the operation ends.

FIG. 12 summarizes the components of an exemplary aggregation modulethat takes advantage of the hierarchy transformation technique describedabove. More specifically, the aggregation module includes an hierarchytransformation module that optimally merges multiple hierarchies into asingle hierarchy that is functionally equivalent to the multiplehierarchies. A second module loads and indexes the base data suppliedfrom the DBMS using the optimal hierarchy generated by the hierarchytransformation module. An aggregation engine performs aggregationoperations on the base data. During the aggregation operations along thedimension specified by the optimal hierarchy, the results of theaggregation operations of the level 0 items may be used in theaggregation operations of the level 1 items, the results of theaggregation operations of the level 1 items may be used in theaggregation operations of the level 2 items, etc. Based on theseoperations, the loading and indexing operations of the base data, alongwith the aggregation become very efficient, minimizing memory andstorage access, and speeding up storing and retrieval operations.

FIG. 13 shows the stand-alone Aggregation Server of the presentinvention as a component of a central data warehouse, serving the dataaggregation needs of URL directory systems, Data Marts, RDBMSs, ROLAPsystems and OLAP systems alike.

The reason for the central multidimensional database's rise to corporatenecessity is that it facilitates flexible, high-performance access andanalysis of large volumes of complex and interrelated data.

A stand-alone specialized aggregation server, simultaneously servingmany different kinds of clients (e.g. data mart, OLAP, URL, RDBMS), hasthe power of delivering an enterprise-wide aggregation in acost-effective way. This kind of server eliminates the roll-upredundancy over the group of clients, delivering scalability andflexibility.

Performance associated with central data warehouse is an importantconsideration in the overall approach. Performance includes aggregationtimes and query response.

Effective interactive query applications require near real-timeperformance, measured in seconds. These application performancestranslate directly into the aggregation requirements.

In the prior art, in case of MOLAP, a full pre-aggregation must be donebefore starting querying. In the present invention, in contrast to priorart, the query directed roll-up (QDR) allows instant querying, while thefull pre-aggregation is done in the background. In cases a fullpre-aggregation is preferred, the currently invented aggregationoutperforms any prior art. For the ROLAP and RDBMS clients, partialaggregations maximize query performance. In both cases fast aggregationprocess is imperative. The aggregation performance of the currentinvention is by orders of magnitude higher than that of the prior art.

The stand-alone scalable aggregation server of the present invention canbe used in any MOLAP system environment for answering questions aboutcorporate performance in a particular market, economic trends, consumerbehaviors, weather conditions, population trends, or the state of anyphysical, social, biological or other system or phenomenon on whichdifferent types or categories of information, organizable in accordancewith a predetermined dimensional hierarchy, are collected and storedwithin a RDBMS of one sort or another. Regardless of the particularapplication selected, the address data mapping processes of the presentinvention will provide a quick and efficient way of managing a MDDB andalso enabling decision support capabilities utilizing the same indiverse application environments.

The stand-alone “cartridge-style” plug-in features of the dataaggregation server of the present invention, provides freedom indesigning an optimized multidimensional data structure and handlingmethod for aggregation, provides freedom in designing a genericaggregation server matching all OLAP vendors, and enablesenterprise-wide centralized aggregation.

The method of Segmented Aggregation employed in the aggregation serverof the present invention provides flexibility, scalability, a conditionfor Query Directed Aggregation, and speed improvement.

The method of Multidimensional data organization and indexing employedin the aggregation server of the present invention provides fast storageand retrieval, a condition for Segmented Aggregation, improves thestoring, handling, and retrieval of data in a fast manner, andcontributes to structural flexibility to allow sliced aggregation andQDR. It also enables the forwarding and single handling of data withimprovements in speed performance.

The method of Query Directed Aggregation (QDR) employed in theaggregation server of the present invention minimizes the data handlingoperations in multi-hierarchy data structures.

The method of Query Directed Aggregation (QDR) employed in theaggregation server of the present invention eliminates the need to waitfor full aggregation to be completed, and provides build-up aggregateddata required for full aggregation.

In another aspect of the present invention, an improved DBMS system(e.g., RDBMS system, object oriented database system orobject/relational database system) is provided that excels in performingtwo distinct functions, namely: the aggregation of data; and thehandling of the resulting data for “on demand” client use. Moreover,because of improved data aggregation capabilities, the DBMS of thepresent invention can be employed in a wide range of applications,including Data Warehouses supporting OLAP systems and the like. Forpurposes of illustration, initial focus will be accorded to the DBMS ofthe present invention. Referring now to FIG. 19 through FIG. 21, thepreferred embodiments of the method and system of the present inventionwill be now described in great detail herein below.

Through this document, the term “aggregation” and “pre-aggregation”shall be understood to mean the process of summation of numbers, as wellas other mathematical operations, such as multiplication, subtraction,division etc. It shall be understood that pre-aggregation operationsoccur asynchronously with respect to the traditional query processingoperations. Moreover, the term “atomic data” shall be understood torefer to the lowest level of data granularity required for effectivedecision making. In the case of a retail merchandising manager, atomicdata may refer to information by store, by day, and by item. For abanker, atomic data may be information by account, by transaction, andby branch.

FIG. 19A illustrates the primary components of an illustrativeembodiment of an DBMS of the present invention, namely: supportmechanisms including a query interface and query handler; a relationaldata store including one or more tables storing at least the atomic data(and possibly summary tables) and a meta-data store for storing adictionary (sometimes referred to as a catalogue or directory); and anMDD Aggregation Module that stores atomic data and aggregated data in aMDDB. The MDDB is a non-relational data structure—it uses other datastructures, either instead of or in addition to tables—to store data.For illustrative purposes, FIG. 19A illustrates an RDBMS wherein therelational data store includes fact tables and a dictionary.

It should be noted that the DBMS typically includes additionalcomponents (not shown) that are not relevant to the present invention.The query interface and query handler service user-submitted queries (inthe preferred embodiment, SQL query statements) forwarded, for example,from a client machine over a network as shown. The query handler andrelational data store (tables and meta-data store) are operably coupledto the MDD Aggregation Module. Importantly, the query handler andintegrated MDD Aggregation Module operate to provide for dramaticallyimproved query response times for data aggregation operations anddrill-downs. Moreover, it is an object of the present invention is tomake user-querying of the non-relational MDDB no different than queryinga relational table of the DBMS, in a manner that minimizes the delaysassociated with queries that involve aggregation or drill downoperations. This object is enabled by providing the novel DBMS systemand integrated aggregation mechanism of the present invention.

FIG. 19B shows the primary components of an illustrative embodiment ofthe MDD Aggregation Module of FIG. 19A, namely: a base data loader forloading the directory and table(s) of relational data store of the DBMS;an aggregation engine for receiving dimension data and atomic data fromthe base loader, a multi-dimensional database (MDDB); a MDDB handler andan SQL handler that operate cooperatively with the query handler of theDBMS to provide users with query access to the MDD Aggregation Module,and a control module for managing the operation of the components of theMDD aggregation module. The base data loader may load the directory andtable(s) of the relational data store over a standard interface (such asOLDB, OLE-DB, ODBC, SQL, API, JDBC, etc.). In this case, the DBMS andbase data loader include components that provide communication of suchdata over these standard interfaces. Such interface components are wellknown in the art. For example, such interface components are readilyavailable from Attunity Corporation, http://www.attunity.com.

During operation, base data originates from the table(s) of the DBMS.The core data aggregation operations are performed by the AggregationEngine; a Multidimensional Data (MDDB) Handler; and a MultidimensionalData Storage (MDDB). The results of data aggregation are efficientlystored in the MDDB by the MDDB Handler. The SQL handler of the MDDAggregation module services user-submitted queries (in the preferredembodiment, SQL query statements) forwarded from the query handler ofthe DBMS. The SQL handler of the MDD Aggregation module may communicatewith the query handler of the DBMS over a standard interface (such asOLDB, OLE-DB, ODBC, SQL, API, JDBC, etc.). In this case, the supportmechanisms of the RDBMS and SQL handler include components that providecommunication of such data over these standard interfaces. Suchinterface components are well known in the art. Aggregation (or drilldown results) are retrieved on demand and returned to the user.

Typically, a user interacts with a client machine (for example, using aweb-enabled browser) to generate a natural language query, that iscommunicated to the query interface of the DBMS, for example over anetwork as shown. The query interface disintegrates the query, viaparsing, into a series of requests (in the preferred embodiment, SQLstatements) that are communicated to the query handler of the DBMS. Itshould be noted that the functions of the query interface may beimplemented in a module that is not part of the DBMS (for example, inthe client machine). The query handler of the DBMS forwards requeststhat involve data stored in the MDD of the MDD Aggregation module to theSQL hander of the MDD Aggregation module for servicing. Each requestspecifies a set of n-dimensions. The SQL handler of the MDD AggregationModule extracts this set of dimensions and operates cooperatively withthe MDD handler to address the MDDB using the set of dimensions,retrieve the addressed data from the MDDB, and return the results to theuser via the query handler of the DBMS.

FIGS. 19C(i) and 19C(ii) is a flow chart illustrating the operations ofan illustrative DBMS of the present invention. In step 601, the basedata loader of the MDD Aggregation Module loads the dictionary (orcatalog) from the meta-data store of the DBMS. In performing thisfunction, the base data loader may utilize an adapter (interface) thatmaps the data types of the dictionary of the DBMS (or that maps astandard data type used to represent the dictionary of the DBMS) intothe data types used in the MDD aggregation module. In addition, the basedata loader extracts the dimensions from the dictionary and forwards thedimensions to the aggregation engine of the MDD Aggregation Module.

In step 603, the base data loader loads table(s) from the DBMS. Inperforming this function, the base data loader may utilize an adapter(interface) that maps the data types of the table(s) of the DBMS (orthat maps a standard data type used to represent the fact table(s) ofthe DBMS) into the data types used in the MDD Aggregation Module. Inaddition, the base data loader extracts the atomic data from thetable(s), and forwards the atomic data to the aggregation engine.

In step 605, the aggregation engine performs aggregation operations(i.e., roll-up operation) on the atomic data (provided by the base dataloader in step 603) along at least one of the dimensions (extracted fromthe dictionary of the DBMS in step 601) and operates cooperatively withthe MDD handler to store the resultant aggregated data in the MDDB. Amore detailed description of exemplary aggregation operations accordingto a preferred embodiment of the present invention is set forth belowwith respect to the QDR process of FIGS. 9A-9C.

In step 607, a reference is defined that provides users with the abilityto query the data generated by the MDD Aggregation Module and/or storedin the MDDB of the MDD Aggregation Module. This reference is preferablydefined using the Create View SQL statement, which allows the user to:i) define a table name (TN) associated with the MDDB stored in the MDDAggregation Module, and ii) define a link used to route SQL statementson the table TN to the MDD Aggregation Module. In this embodiment, theview mechanism of the DBMS enables reference and linking to the datastored in the MDDB of the MDD Aggregation Engine as illustrated in FIG.6(E). A more detailed description of the view mechanism and the CreateView SQL statement may be found in C. J. Date, “An Introduction toDatabase Systems,” Addison-Wesley, Seventh Edition, 2000, pp. 289-326,herein incorporated by reference in its entirety. Thus, the viewmechanism enables the query handler of the DBMS system to forward anySQL query on table TN to the MDD aggregation module via the associatedlink. In an alternative embodiment, a direct mechanism (e.g., NA triggermechanism) may be used to enable the DBMS system to reference and linkto the data generated by the MDD Aggregation Module and/or stored in theMDDB of the MDD Aggregation Engine as illustrated in FIG. 6F. A moredetailed description of trigger mechanisms and methods may be found inC. J. Date, “An Introduction to Database Systems,” Addison-Wesley,Seventh Edition, 2000, pp. 250, 266, herein incorporated by reference inits entirety.

In step 609, a user interacts with a client machine to generate a query,and the query is communicated to the query interface. The queryinterface generate one or more SQL statements. These SQL statements mayrefer to data stored in tables of the relational datastore, or may referto the reference defined in step 607 (this reference refers to the datastored in the MDDB of the MDD Aggregation Module). These SQLstatement(s) are forwarded to the query handler of the DBMS.

In step 611, the query handler receives the SQL statement(s); andoptionally transforms such SQL statement(s) to optimize the SQLstatement(s) for more efficient query handling. Such transformations arewell known in the art. For example, see Kimball, Aggregation NavigationWith (Almost) No MetaData”, DBMS Data Warehouse Supplement, August 1996,available at http://www.dbmsmag.com/9608d54.html.

In step 613: the query handler determines whether the received SQLstatement(s) [or transformed SQL statement(s)] is on the referencegenerated in step 607. If so, operation continues to step 615; otherwisenormal query handling operations continue in step 625 wherein therelational datastore is accessed to extract, store, and/or manipulatethe data stored therein as directed by the query, and results arereturned back to the user via the client machine, if needed.

In step 615, the received SQL statement(s) [or transformed SQLstatement(s)] is routed to the MDD aggregation engine for processing instep 617 using the link for the reference as described above withrespect to step 607.

In step 617, the SQL statement(s) is received by the SQL handler of theMDD Aggregation Module, wherein a set of one or more N-dimensionalcoordinates are extracted from the SQL statement. In performing thisfunction, SQL handler may utilize an adapter (interface) that maps thedata types of the SQL statement issued by query handler of the DBMS (orthat maps a standard data type used to represent the SQL statementissued by query handler of the DBMS) into the data types used in the MDDaggregation module.

In step 619, the set of N-dimensional coordinates extracted in step 617are used by the MDD handler to address the MDDB and retrieve thecorresponding data from the MDDB.

Finally, in step 621, the retrieved data is returned to the user via theDBMS (for example, by forwarding the retrieved data to the SQL handler,which returns the retrieved data to the query handler of the DBMSsystem, which returns the results of the user-submitted query to theuser via the client machine), and the operation ends.

It should be noted that the table data (base data), as it arrives fromDBMS, may be analyzed and reordered to optimize hierarchy handling,according to the unique method of the present invention, as describedabove with reference to FIGS. 11A, 11B, and 11C.

Moreover, the MDD control module of the MDD Aggregation Modulepreferably administers the aggregation process according to the methodillustrated in FIGS. 9A and 9B. Thus, in accordance with the principlesof the present invention, data aggregation within the DBMS can becarried out either as a complete pre-aggregation process, where the basedata is fully aggregated before commencing querying, or as a querydirected roll-up (QDR) process, where querying is allowed at any stageof aggregation using the “on-the-fly” data aggregation process of thepresent invention. The QDR process will be described hereinafter ingreater detail with reference to FIG. 9C. The response to a request(i.e. a basic component of a client query) requiring “on-the-fly” dataaggregation, or requiring access to pre-aggregated result data via theMDD handler is provided by a query/request serving mechanism of thepresent invention within the MDD control module, the primary operationsof which are illustrated in the flow chart of FIG. 6D. The function ofthe MDD Handler is to handle multidimensional data in the storage(s)module in a very efficient way, according to the novel method of thepresent invention, which will be described in detail hereinafter withreference to FIGS. 10A and 10B.

The SQL handling mechanism shown in FIG. 6D is controlled by the MDDcontrol module. Requests are queued and served one by one. If therequired data is already pre-calculated, then it is retrieved by the MDDhandler and returned to the client. Otherwise, the required data iscalculated “on-the-fly” by the aggregation engine, and the result movedout to the client, while simultaneously stored by the MDD handler, shownin FIG. 6C.

As illustrated in FIG. 19G, the DBMS of the present invention asdescribed above may be logically partitioned into a relational part anda non-relational part. The relational part includes the relationaldatastore (e.g., table(s) and dictionary) and support mechanisms (e.g.,query handling services). The non-relational part includes the MDDAggregation Module. As described above, bi-directional data flow occursbetween the relational part and the non-relational part as shown. Morespecifically, during data load operations, data is loaded from therelational part (i.e., the relational datastore) into the non-relationalpart, wherein it is aggregated and stored in the MDDB. And during queryservicing operations, when a given query references data stored in theMDDB, data pertaining to the query is generated by the non-relationalpart (e.g., generated and/or retrieved from the MDDB) and supplied tothe relational part (e.g., query servicing mechanism) for communicationback to the user. Such bi-directional data flow represents an importantdistinguishing feature with respect to the prior art. For example, inthe prior art MOLAP architecture as illustrated in FIG. 1B,unidirectional data flows occurs from the relational data base (e.g.,the Data Warehouse RDBMS system) into the MDDB during data loadingoperations.

FIGS. 20A and 20B outline two different implementations of the DBMS ofthe present invention. In both implementations, the query handler of theDBMS system supplies aggregated results retrieved from the MDD to aclient.

FIG. 20A shows a separate-platform implementation of the DBMS system ofthe illustrative embodiment shown in FIG. 19A, wherein the relationalpart of the DBMS reside on a separate hardware platform and/or OS systemfrom that used to run the non-relational part (MDD Aggregation Module).In this type of implementation, it is even possible to run parts of theDBMS system and the MDD Aggregation Module on different-type operatingsystems (e.g. NT, Unix, MAC OS).

FIG. 20B shows a common-platform implementation of the DBMS system ofthe illustrative embodiment shown in FIG. 20A, wherein the relationalpart of the DBMS share the same hardware platform and operating system(OS) that is used to run the non-relational part (MDD AggregationModule).

FIG. 21 shows the improved DBMS (e.g., RDBMS) of the present inventionas a component of a data warehouse, serving the data storage andaggregation needs of a ROLAP system (or other OLAP systems alike).Importantly, the improved DBMS of the present invention providesflexible, high-performance access and analysis of large volumes ofcomplex and interrelated data. Moreover, the improved Data WarehouseDBMS of the present invention can simultaneously serve many differentkinds of clients (e.g. data mart, OLAP, URL) and has the power ofdelivering an enterprise-wide data storage and aggregation in acost-effective way. This kind of system eliminates redundancy over thegroup of clients, delivering scalability and flexibility. Moreover, theimproved DBMS of the present invention can be used as the data storecomponent of in any informational database system as described above,including data analysis programs such as spread-sheet modeling programs,serving the data storage and aggregation needs of such systems.

FIG. 22 shows an embodiment of the present invention wherein the DBMS(e.g., RDBMS) of the present invention is a component of a datawarehouse—OLAP system. The DBMS operates as a traditional datawarehouse, serving the data storage and aggregation needs of anenterprise. In addition, the DBMS includes integrated OLAP AnalysisLogic (and preferably an integrated Presentation Module not shown) thatoperates cooperatively with the query handling of the DBMS system andthe MDD Aggregation Module to enable users of the DBMS system to executemultidimensional reports (e.g., ratios, ranks, transforms, dynamicconsolidation, complex filtering, forecasts, query governing,scheduling, flow control, pre-aggregate inferencing, denomalizationsupport, and/or table partitioning and joins) and preferably performtraditional OLAP analyses, (grids, graphs, maps, alerts, drill-down,data pivot, data surf, slice and dice, print). Importantly, the improvedDBMS of the present invention provides flexible, high-performance accessand analysis of large volumes of complex and interrelated data.Moreover, the improved DBMS of the present invention can simultaneouslyserve many different kinds of clients (e.g. data mart, other OLAPsystems, URL-Directory Systems) and has the power of deliveringenterprise-wide data storage and aggregation and OLAP analysis in acost-effective way. This kind of system eliminates redundancy over thegroup of clients, delivering scalability and flexibility. Moreover, theimproved DBMS of the present invention can be used as the data storecomponent of in any informational database system as described above,serving the data storage and aggregation needs of such systems.

Functional Advantages Gained by the Improved DBMS of the PresentInvention

The features of the DBMS of the present invention, provides fordramatically improved response time in handling queries issued to theDBMS that involve aggregation, thus enabling enterprise-wide centralizedaggregation. Moreover, in the preferred embodiment of the presentinvention, users can query the aggregated data in an manner no differentthan traditional queries on the DBMS.

The method of Segmented Aggregation employed by the novel DBMS of thepresent invention provides flexibility, scalability, the capability ofQuery Directed Aggregation, and speed improvement.

Moreover, the method of Query Directed Aggregation (QDR) employed by thenovel DBMS of the present invention minimizes the data handlingoperations in multi-hierarchy data structures, eliminates the need towait for full aggregation to be complete, and provides for build-up ofaggregated data required for full aggregation.

It is understood that the System and Method of the illustrativeembodiments described herein above may be modified in a variety of wayswhich will become readily apparent to those skilled in the art of havingthe benefit of the novel teachings disclosed herein. All suchmodifications and variations of the illustrative embodiments thereofshall be deemed to be within the scope and spirit of the presentinvention as defined by the Claims to Invention appended hereto.

1. An on-line analytical processing (OLAP) system, comprising: a dataaggregation module for servicing queries directed towards highdimensionality sparse data sets, said data aggregation module including:(1) a multi-dimensional datastore, (2) an hierarchy transformationmodule for receiving an original hierarchical database structure of saidOLAP system defining parent-child relationships of levels withindimensions and converting said hierarchical database structure into afunctionally equivalent hierarchical database structure optimized forrapid aggregation, storage and retrieval of sparse data; and (3) anaggregation engine for aggregating said large data sets, includingsparse data, according to said functionally equivalent hierarchy.
 2. TheOLAP system of claim 1, further comprising a load and indexing modulefor organizing said multi-dimensional data store into autonomic segmentscapable of being rolled up in different sequences according to saidfunctionally equivalent hierarchy, said autonomic segments being storedas records indexed such that each data segment is capable of beingindependently loaded into a main memory and each data record has a sizethat is comparatively small compared to a maximum data size of said diskmemory space.
 3. The OLAP system of claim 2, wherein said aggregationmodule has a mode of operation in which data is aggregated on-the-fly bysaid aggregation engine to service a query statement by determining aroll-up order based on any previously pre-aggregated sparse data andloading into said main memory a set of memory data segments having datapoints for performing aggregation on-the-fly to service the querystatement.
 4. The OLAP system of claim 1 further comprising an OLAPserver, wherein said aggregation module serves as a complimentaryaccelerator to said OLAP server.
 5. An on-line analytical processing(OLAP) system, comprising: a data aggregation module to service queriesdirected towards high dimensionality sparse data sets, said dataaggregation module including: (1) a multi-dimensional datastore, (2) anhierarchy transformation module a for converting an originalhierarchical database structure of said OLAP system into a functionallyequivalent hierarchical database structure optimized for large datasets, including sparse data having a low density of data points, saidfunctionally equivalent hierarchical database structure being used toperform data indexing and aggregation operations such that groups ofrelated data points at different stages of an aggregation process areorganized into sub-units of memory storage that are individuallyaccessible from a memory space of said multi-dimensional datastore; and(3) an aggregation engine having a mode of operation in which data isaggregated on-the-fly to service a query statement by identifying a setof said sub-units of memory storage having partially pre-aggregated datato perform an aggregation on-the-fly, loading said set of sub-units ofmemory storage into a main memory, and performing an aggregationon-the-fly to service the query statement.
 6. The OLAP system of claim5, wherein said functionally equivalent hierarchical database structureis optimized for memory and storage access.
 7. The OLAP system of claim5, wherein said functionally equivalent hierarchical database structureis functionally equivalent to multiple hierarchies in at least onedimension.
 8. The OLAP system of claim 5, wherein said functionallyequivalent hierarchical database structure is chosen to minimize datastorage requirements
 9. The OLAP system of claim 5, wherein said OLAPsystem verifies the integrity of said functionally equivalenthierarchical database structure and reports errors.
 10. The OLAP systemof claim 5, wherein said OLAP system verifies the integrity of saidfunctionally equivalent hierarchical database structure and fixeserrors.
 11. The OLAP system of claim 5, wherein said sub-units arerecords within a directory file.
 12. The OLAP system of claim 5 whereinsaid sub-units are slices of multi-dimensional data.
 13. The OLAP systemof claim 12, wherein each slice is a N−1 dimensional slice.
 14. The OLAPsystem of claim 5, further comprising an OLAP server, said aggregationmodule serving as a complimentary accelerator to said OLAP server. 15.An on-line analytical processing (OLAP) system, comprising: a dataaggregation module for use as a complimentary accelerator with an OLAPserver to improve the servicing of high dimensionality sparse data sets,said data aggregation module including: (1) a multi-dimensionaldatastore, (2) a load and indexing module for organizing saidmulti-dimensional datastore into records capable of being independentlyloaded into a main memory with each record having a size that is smallcompared with a maximum size of said multi-dimensional data store andeach record corresponding to an autonomic segment, each autonomicsegment storing base data or aggregated data with autonomic segmentscorresponding to partially pre-aggregated data capable of being rolledup in different sequences; and (3) an aggregation engine performing dataaggregation utilizing said autonomic segments to limit the amount ofsimultaneously handled data, said aggregation module having a mode ofoperation in which data is aggregated on-the-fly to service a givenquery statement by determining a rollup order of a set of records havingautonomic segments capable of being rolled up to service the given querystatement, loading into a main memory said set of records, andperforming a data aggregation operation to service the query request.16. The OLAP system of claim 15, further comprising an hierarchytransformation module for receiving a predetermined dimensionalhierarchy defining parent-child relationships and converting saidpredetermined dimensional hierarchy into a functionally equivalenthierarchy optimized for aggregating large data sets, including sparsedata, and wherein said aggregation module utilizes said functionallyequivalent hierarchy for performing data indexing operations and dataaggregation operations.
 17. The OLAP system of claim 15, wherein saidOLAP system verifies the integrity of said functionally equivalenthierarchy and reports errors.
 18. The OLAP system of claim 15, whereineach segment corresponds to a group of related data points.
 19. The OLAPsystem of claim 15, wherein each segment is a slice of multidimensionaldata.
 20. The OLAP system of claim 15 wherein said records are indexedby a directory file.
 21. A method for accelerating the servicing of highdimensionality sparse data sets in an on-analytical processing (OLAP)system, comprising: receiving an original hierarchical databasestructure of said OLAP system; and converting said original hierarchicaldatabase structure into a functionally equivalent hierarchical databasestructure optimized for performing data storage and aggregationoperations on large data sets, including sparse data.
 22. The method ofclaim 21, further comprising: performing a segmented aggregation processbased on said functionally equivalent hierarchical database structure inwhich data is segmented into segments of sparse data capable of beingrolled up in different rollup orders.
 23. The method of claim 22,further comprising: performing a partial pre-aggregation according tosaid functionally equivalent hierarchical database structure to generatean initial set of segments stored as data records in a memory space of amulti-dimensional datastore; in response to receiving a query statementrequiring data that has not been pre-aggregated, determining a rolluporder of autonomic segments to service the query statement on-the-flybased on said initial set of autonomic segments; loading into a mainmemory records corresponding to a subset of said initial set ofautonomic records required to perform said rollup; and performing saidrollup to aggregate data on-the-fly to service the query statement. 24.A method for accelerating the servicing of high dimensionality sparsedata sets in an on-analytical processing (OLAP) system, comprising:receiving an hierarchical database structure of said OLAP system;organizing an aggregation process for autonomic segments capable ofbeing rolled up in different rollup orders; performing a partialpre-aggregation according to said aggregation process to generate aninitial set of autonomic segments stored as data records in a memoryspace of a multi-dimensional datastore; in response to receiving a querystatement requiring data that has not been pre-aggregated, determining arollup order of a subset of autonomic segments to service the querystatement on-the-fly based on said initial set of autonomic segments;loading into a main memory records corresponding to said subset of saidinitial set of autonomic records require to perform said rollup; andperforming said rollup to aggregate data on-the-fly to service the querystatement.